Although AI and advanced analytics are quickly becoming non-negotiable in marketing, these tools are only as powerful as the data you feed them. If your marketing data is inconsistent, incomplete, or outdated, AI analytics will amplify the problem, faster, and with more confidence. Despite this, CMOs estimate that 45% of the data their teams use to drive decisions is poor quality.
Luckily, even simple data safeguards can go a long way to ensuring high quality, AI-ready data. This guide pulls together best practices Adverity teams regularly share with customers preparing marketing data for AI use cases, including conversational analytics and model-driven workflows.
We’ll use the six dimensions of data quality as the framework: accuracy, completeness, consistency, uniqueness, timeliness, and validity. Check out the webinar below or read on for highlights.
Why AI raises the bar for data quality
Marketing teams have always lived with data imperfections. The difference is that AI amplifies the consequences. Inconsistencies become “different truths.” If the same market, channel, or campaign is represented in multiple ways, AI may interpret them as separate entities. Missing context becomes misinterpretation. AI can’t reliably infer what a field means if your dataset doesn’t describe it clearly. And stale or partial data produces confident wrong answers. If a model runs before upstream fetches complete, you end up training and analyzing on gaps. All that to say that data which might have sufficed for reporting often doesnt hold up to the standards AI requires.
The six dimensions of data quality
Data quality is a fitness check for your data. Is it accurate, complete, consistent, unique, timely, and valid? When your data meets these standards, it becomes a trusted foundation for decision-making and analysis.
Now, the practical part. Here's how to meet the data standards for AI analytics.
1) Accuracy: make sure values reflect reality
What it means:
Accuracy is how closely your data reflects real-world events, without errors or distortions.
Why it matters for AI:
AI systems learn patterns from what you give them. If the inputs are wrong, the outputs will be wrong, only faster. Accuracy issues are especially risky when you’re using AI to generate recommendations or summaries that people act on.
What to do:
- Validate and cleanse at the source. Regularly cross-check key metrics against trusted sources (e.g., platform UIs) to catch ingestion or mapping errors early.
- Investigate spikes and dips, not just performance. If you see unexpected jumps in clicks, spend, or impressions, sanity-check whether it’s a real campaign event or a pipeline issue (e.g., a filter changed, duplicated ingestion, or a connector misconfiguration).
- Treat anomalies as a red flag. AI can normalize bad patterns over time if anomalies aren’t investigated.
2) Completeness: ensure required fields are present
What it means:
Completeness measures whether all necessary data is present.
Why it matters for AI:
Missing values don’t just reduce reporting quality. They can confuse what AI learns is normal, which can affect trend interpretation and recommendations over time.
What to do:
- Add validation checks at ingestion points. Flag missing fields early, before they flow downstream.
- Use audits to spot persistent gaps. If a field repeatedly comes in blank or null, treat it as a signal. Either fix the upstream issue or retire the field if it’s no longer usable.
- Keep a minimum viable dataset mindset. Especially early on, ensure the fields that matter most are complete before expanding the dataset.
3) Consistency: standardize how data is labeled and formatted
What it means:
Consistency ensures data is uniformly formatted and synchronized across systems.
Why it matters for AI:
This is one of the most common reasons AI analysis goes off track. If one team labels a market as “New York City,” another uses “NY,” and another uses “NYC,” AI may treat these as separate categories and skew performance insights.
Consistency issues also show up in how people query data. In conversational analytics, users often search by terms like “UK” versus “United Kingdom.” If your data isn’t standardized, retrieval and comparisons suffer.
What to do:
- Standardize categorical fields and naming conventions. Define approved terms (markets, brands, lifecycle stages, campaign naming patterns) and align teams on best practices.
- Assume enforcement will be imperfect, then design for it. Large orgs and agencies struggle to enforce naming conventions manually. The best approach is to combine guidance with automated standardization where possible.
- Review changes from data sources. Ad platforms frequently add, remove, or rename fields. Small inconsistencies can snowball into reporting drift and model confusion if not caught.
4) Uniqueness: prevent duplicates and unnecessary noise
What it means
Uniqueness means your data should not contain duplicate entries.
Why it matters for AI
Duplicates inflate metrics and muddy patterns. In AI workflows, too much redundant or low-value data can also introduce noise, slow analysis, and increase the risk of errors being interpreted as meaningful signals.
What to do
- Deduplicate where duplicates commonly occur. Keep an eye on customer records, campaign records, and any joins or transformations that might create duplicate rows.
- Keep data lean at the field level. Before adding new mapped fields, calculated fields, or custom fields, pressure-test whether you actually need them. If a field isn’t used, it can still create confusion downstream.
- Archive what you don’t need. Datasets kept for contingency rather than active use are risky in AI contexts. Start tighter, then expand when you’re confident.
5) Timeliness: keep data fresh enough to support decisions
What it means
Timeliness is how up-to-date your data is relative to the decisions and models depending on it.
Why it matters for AI
AI outputs depend heavily on timing. If your model or reporting layer runs before upstream fetches are complete, you risk training on stale data and baking that into outputs.
What to do
- Buffer your data collection before downstream updates. If a model update runs at 2:00 a.m., aim to have all data fetched and complete by 1:00 a.m. to reduce the risk of partial inputs.
- Avoid overlapping data fetch schedules. Overlaps can create performance issues and increase the likelihood of incomplete data when you need it most.
- Run heavy historical fetches off-hours. Large backfills can take time and disrupt day-to-day operations if they run while teams are actively working.
- Use smart scheduling where possible. A scheduling approach that accounts for API performance, data volume, and other account schedules reduces manual guesswork and helps ensure data arrives by a defined deadline.
6) Validity: ensure data conforms to rules and expected structure
What it means
Validity is whether data conforms to the required format, structure, or rules (correct date formats, mandatory fields, correct types, expected values).
Why it matters for AI
Invalid data can pass through pipelines unnoticed, then surface later as inconsistent analysis or broken workflows. In AI contexts, validity issues can cause wrong interpretations and brittle outputs.
What to do
- Set up alerting as a guardrail. The goal is to catch issues early, before they propagate into downstream analysis or AI workflows. Useful alerts include:
- Data stream or transformation failures
- Expired authorizations (to avoid data gaps)
- Mapping changes or new values introduced unexpectedly
- Data stream or transformation failures
- Watch for schema drift. If field names or structures change (common with ad platforms), validate mappings and rules before downstream tools start reading the new shape.
- Handle formatting requirements for downstream tools. Some analytics providers (including MMM tools) require very specific column names and column orders. If your dataset isn’t structured correctly, it may be rejected or misread.
Common AI-readiness traps to avoid
Here are a few common issues we've seen happen when teams start testing AI workflows, and some tips on how to deal with them.
1. Test data streams accidentally left on
When experimentation ramps up, test streams can get left active or re-enabled by another user later. That can inflate data and create false trends. Archiving test streams clearly so they don’t get reactivated accidentally will help with this. Find a way to make tests visually obvious in a long list.
2. Too much data too soon
More data isn’t automatically better for AI. Excess fields, redundant datasets, and unnecessary transformations create noise and eat up resources. Start with a dataset you can trust, then expand.
3. Null values treated as normal
Repeated blanks and nulls create reporting holes, and can teach models to expect missing values, which affects interpretation of trends and anomalies.
4. Outliers ignored because they look like a win
If performance suddenly spikes, don’t automatically assume it’s a campaign success. Check whether it’s a pipeline issue (duplicate ingestion, broken filters, mapping changes) before you let AI interpret it as a meaningful pattern.
AI readiness is an ongoing task
Getting your marketing data AI ready isn’t a one-off setup task. In many cases, maintaining long-term accuracy and trust is harder than the initial build.
Marketing data changes constantly. Platforms introduce new fields, teams adopt new naming habits, and business rules evolve. The most reliable AI outcomes come from teams that stay proactive about monitoring, auditing, and fixing issues at the root cause, not just patching symptoms downstream.
